Intelligent robust tracking control for multi-arm mobile manipulators using a fuzzy cerebellar model articulation controller neural network

Abstract

This article originally analyses intelligent robust tracking for multi-arm fruit-harvesting mobile manipulators (MAFHMMs) with delayed angle-velocity uncertainties. The MAFHMMs are composed of two parts: a crawler-type mobile platform and a four-arm harvesting manipulator. The method proposed here does not require a matching condition for the non-linear uncertainties. A fuzzy cerebellar model articulation controller (CMAC) neural network system is used to approximate an unknown controlled system from the strategic manipulation of the model following the tracking errors. In addition, an adaptive robust compensator is presented to compensate for the uncertainties. Based on the Lyapunov stability theory and neural network approximation capability, several sufficient conditions are derived, which guarantee the convergence of the closed-loop error system. Both simulation and experimental results show the superior control performance of the proposed intelligent control method.